Autonomous Driving Robot

The purpose of our project is to train the NVIDIA Robot - which is a pre-built robot with a complete Artificial Intelligent (AI) development environment based on the tiny Jetson Nano computer chip, hardcoded using Python platform. Therefore, the robot has the ability to avoid objects (collision avoidance) and keep track to drive safely within the lane lines (road following). Moreover, this autonomous robot is also able to follow the instruction signs and traffic signs such as a left turn, right turn, stop, and go to a pre-set destination. The autonomous training methodology of the robot is based on deep learning neural networks. In this project, the robot is built, implemented, and tested on our setup environment using a cardboard based surface marked with white lines and with instruction signs.

Cybersecurity Risk Assessment Tool for Social Media Profiles

Julia Ma, Sara Cho, Taner TuncerAdvisor: Dr. Hang Liu

For this project, we created a Python program that analyzes a user’s social media profile and identifies possible security risks. The program uses data from a participant’s Facebook Timeline as input. It employs state-of-the-art text processing techniques to analyze the user's Facebook data. Specifically, the program employs a Naïve Bayes machine learning algorithm to build a model that identifies vulnerable personal information. The output is an assessment of the potentially risky behaviors that leave the user's account open to attacks from anyone with a full or limited access to one’s social media data. To protect the user's privacy, the user's name and results will not be stored. This program could be used to raise users’ awareness of their vulnerability to online attacks and to educate users on safe social media practices.

Eye Blink Detection Project

This project aims to implement the Eye Aspect Ratio (EAR) technique to detect and count eye-blinks under various light conditions (daytime and nighttime). EAR is the ratio between the height and width of the eyes at different locations. This technique will be applied to paralyzed patients with Amyotrophic lateral sclerosis (ALS). Paralyzed patients can use this application for emergency purposes by flickering their eyelids to trigger an alarm. In this project prototype a Python software equipped with a graphical user interface (GUI) will be created to detect and count the eye blinks of a certain user. The code will run on a laptop computer equipped with a night vision camera. This allows the system to be portable, low cost, and compact.

Smart Magic Mirror

The magic mirror uses a two-way mirror’s ability to transfer light onto the face of the mirror from a source on the back—in this case, an LCD screen. The screen, pressed against the back of the mirror, will be controlled by a Raspberry Pi in order to display various information while still preserving the functionality of the mirror. The goal is to streamline the intake of data—particularly during the morning process. Having this information available would be substantially more convenient for consumers. In addition to the base model, we will be implementing a profile feature leveraging facial recognition technology. This addition would increase accessibility, especially due to its ability to have different layouts based on the user’s face. The primary objective of the product, ultimately, is to save the consumers’ time. A secondary objective would be making the layout and displays easily and widely customizable.

Smart Solar Panel

Energy resources play one of the essential roles in human life; one of them is solar energy. However, solar energy is considered to be one of the least-efficient source of power. Therefore, we want to design a high-efficient solar system that is portable for everyone to use easily. Thus, the system will be designed as small as possible in order to minimize the weight, will be easy to assemble and the output power will be improved by using tracking-system. To distinguish our project from the existing solar-system, our goal was to make it as portable, cheap, and high-efficient as possible. Our proof-of-concept system uses the appropriate power for each circuit, components to reduce the waste-energy consumption and compact all of them together to optimize the system, and a display-system for users to know the status of the system such as how much time they have left with the current-plugging devices.